Effectively Representing Short Text via the Improved Semantic Feature Space Mapping

Abstract

Short text representation (STR) has attracted increasing interests recently with the rapid growth of Web and social media data existing in short text form. In this paper, we present a new method using an improved semantic feature space mapping to effectively represent short texts. Firstly, semantic clustering of terms is performed based on statistical analysis and word2vec, and the semantic feature space can then be represented via the cluster center. Then, the context information of terms is integrated with the semantic feature space, based on which three improved similarity calculation methods are established. Thereafter the text mapping matrix is constructed for short text representation learning. Experiments on both Chinese and English test collections show that the proposed method can well reflect the semantic information of short texts and represent the short texts reasonably and effectively.